Are Minimal Radial Distortion Solvers Necessary for Relative Pose Estimation?

• URL: http://arxiv.org/abs/2410.05984v1
• Date: Tue, 8 Oct 2024 12:30:29 GMT
• Title: Are Minimal Radial Distortion Solvers Necessary for Relative Pose Estimation?
• Authors: Charalambos Tzamos, Viktor Kocur, Yaqing Ding, Torsten Sattler, Zuzana Kukelova,
• Abstract summary: This paper compares radial distortion solvers with a simple-to-implement approach that combines an efficient pinhole solver with sampled radial distortion parameters. Experiments on multiple datasets and RANSAC variants show that this simple approach performs similarly or better than the most accurate minimal distortion solvers.
• Score: 39.593903427245664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Estimating the relative pose between two cameras is a fundamental step in many applications such as Structure-from-Motion. The common approach to relative pose estimation is to apply a minimal solver inside a RANSAC loop. Highly efficient solvers exist for pinhole cameras. Yet, (nearly) all cameras exhibit radial distortion. Not modeling radial distortion leads to (significantly) worse results. However, minimal radial distortion solvers are significantly more complex than pinhole solvers, both in terms of run-time and implementation efforts. This paper compares radial distortion solvers with a simple-to-implement approach that combines an efficient pinhole solver with sampled radial distortion parameters. Extensive experiments on multiple datasets and RANSAC variants show that this simple approach performs similarly or better than the most accurate minimal distortion solvers at faster run-times while being significantly more accurate than faster non-minimal solvers. We clearly show that complex radial distortion solvers are not necessary in practice. Code and benchmark are available at https://github.com/kocurvik/rd.

Related papers

• DiffIR: Efficient Diffusion Model for Image Restoration [108.82579440308267]
  Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis process into a sequential application of a denoising network. Traditional DMs running massive iterations on a large model to estimate whole images or feature maps is inefficient for image restoration. We propose DiffIR, which consists of a compact IR prior extraction network (CPEN), dynamic IR transformer (DIRformer), and denoising network.
  arXiv  Detail & Related papers  (2023-03-16T16:47:14Z)
• An Adaptive Method for Camera Attribution under Complex Radial Distortion Corrections [77.34726150561087]
  In-camera or out-camera software/firmware alters the supporting grid of the image so as to hamper PRNU-based camera attribution. Existing solutions to deal with this problem try to invert/estimate the correction using radial transformations parameterized with few variables in order to restrain the computational load. We propose an adaptive algorithm that by dividing the image into concentric annuli is able to deal with sophisticated corrections like those applied out-camera by third party software like Adobe Lightroom, Photoshop, Gimp and PT-Lens.
  arXiv  Detail & Related papers  (2023-02-28T08:44:00Z)
• RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging [62.315673415889314]
  This paper proposes a deep recurrent Rotation Averaging Graph (RAGO) for Multiple Rotation Averaging (MRA) Our framework is a real-time learning-to-optimize rotation averaging graph with a tiny size deployed for real-world applications.
  arXiv  Detail & Related papers  (2022-12-14T13:19:40Z)
• Self-Calibrating Neural Radiance Fields [68.64327335620708]
  We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model consists of a pinhole model, a fourth order radial distortion, and a generic noise model that can learn arbitrary non-linear camera distortions.
  arXiv  Detail & Related papers  (2021-08-31T13:34:28Z)
• Efficient Real-Time Radial Distortion Correction for UAVs [1.7149364927872015]
  We present a novel algorithm for onboard radial distortion correction for unmanned aerial vehicles (UAVs) equipped with an inertial measurement unit (IMU) This approach makes calibration procedures redundant, thus allowing for exchange of optics extemporaneously. We propose a fast and robust minimal solver for simultaneously estimating the focal length, radial distortion profile and motion parameters from homographies.
  arXiv  Detail & Related papers  (2020-10-08T18:34:56Z)
• A Deep Ordinal Distortion Estimation Approach for Distortion Rectification [62.72089758481803]
  We propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. We design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation.
  arXiv  Detail & Related papers  (2020-07-21T10:03:42Z)
• Quasi-Newton Solver for Robust Non-Rigid Registration [35.66014845211251]
  We propose a formulation for robust non-rigid registration based on a globally smooth robust estimator for data fitting and regularization. We apply the majorization-minimization algorithm to the problem, which reduces each iteration to solving a simple least-squares problem with L-BFGS.
  arXiv  Detail & Related papers  (2020-04-09T01:45:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.